{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LangGraph Retrieval Agent\n",
    "\n",
    "We can implement\n",
    "[Retrieval Agents](https://js.langchain.com/docs/tutorials/rag/)\n",
    "in [LangGraph](https://js.langchain.com/docs/langgraph)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "### Load env vars\n",
    "\n",
    "Add a `.env` variable in the root of the `./examples` folder with your\n",
    "variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "// import dotenv from 'dotenv';\n",
    "\n",
    "// dotenv.config();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install dependencies\n",
    "\n",
    "```bash\n",
    "npm install cheerio zod zod-to-json-schema langchain @langchain/openai @langchain/core @langchain/community @langchain/textsplitters\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retriever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { CheerioWebBaseLoader } from \"@langchain/community/document_loaders/web/cheerio\";\n",
    "import { RecursiveCharacterTextSplitter } from \"@langchain/textsplitters\";\n",
    "import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
    "import { OpenAIEmbeddings } from \"@langchain/openai\";\n",
    "\n",
    "const urls = [\n",
    "  \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
    "  \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
    "  \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
    "];\n",
    "\n",
    "const docs = await Promise.all(\n",
    "  urls.map((url) => new CheerioWebBaseLoader(url).load()),\n",
    ");\n",
    "const docsList = docs.flat();\n",
    "\n",
    "const textSplitter = new RecursiveCharacterTextSplitter({\n",
    "  chunkSize: 500,\n",
    "  chunkOverlap: 50,\n",
    "});\n",
    "const docSplits = await textSplitter.splitDocuments(docsList);\n",
    "\n",
    "// Add to vectorDB\n",
    "const vectorStore = await MemoryVectorStore.fromDocuments(\n",
    "  docSplits,\n",
    "  new OpenAIEmbeddings(),\n",
    ");\n",
    "\n",
    "const retriever = vectorStore.asRetriever();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent state\n",
    "\n",
    "We will define a graph.\n",
    "\n",
    "You may pass a custom `state` object to the graph, or use a simple list of\n",
    "`messages`.\n",
    "\n",
    "Our state will be a list of `messages`.\n",
    "\n",
    "Each node in our graph will append to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { Annotation } from \"@langchain/langgraph\";\n",
    "import { BaseMessage } from \"@langchain/core/messages\";\n",
    "\n",
    "const GraphState = Annotation.Root({\n",
    "  messages: Annotation<BaseMessage[]>({\n",
    "    reducer: (x, y) => x.concat(y),\n",
    "    default: () => [],\n",
    "  })\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { createRetrieverTool } from \"langchain/tools/retriever\";\n",
    "import { ToolNode } from \"@langchain/langgraph/prebuilt\";\n",
    "\n",
    "const tool = createRetrieverTool(\n",
    "  retriever,\n",
    "  {\n",
    "    name: \"retrieve_blog_posts\",\n",
    "    description:\n",
    "      \"Search and return information about Lilian Weng blog posts on LLM agents, prompt engineering, and adversarial attacks on LLMs.\",\n",
    "  },\n",
    ");\n",
    "const tools = [tool];\n",
    "\n",
    "const toolNode = new ToolNode<typeof GraphState.State>(tools);"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nodes and Edges\n",
    "\n",
    "Each node will -\n",
    "\n",
    "1/ Either be a function or a runnable.\n",
    "\n",
    "2/ Modify the `state`.\n",
    "\n",
    "The edges choose which node to call next.\n",
    "\n",
    "We can lay out an agentic RAG graph like this:\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { END } from \"@langchain/langgraph\";\n",
    "import { pull } from \"langchain/hub\";\n",
    "import { z } from \"zod\";\n",
    "import { ChatPromptTemplate } from \"@langchain/core/prompts\";\n",
    "import { ChatOpenAI } from \"@langchain/openai\";\n",
    "import { AIMessage, BaseMessage } from \"@langchain/core/messages\";\n",
    "\n",
    "/**\n",
    " * Decides whether the agent should retrieve more information or end the process.\n",
    " * This function checks the last message in the state for a function call. If a tool call is\n",
    " * present, the process continues to retrieve information. Otherwise, it ends the process.\n",
    " * @param {typeof GraphState.State} state - The current state of the agent, including all messages.\n",
    " * @returns {string} - A decision to either \"continue\" the retrieval process or \"end\" it.\n",
    " */\n",
    "function shouldRetrieve(state: typeof GraphState.State): string {\n",
    "  const { messages } = state;\n",
    "  console.log(\"---DECIDE TO RETRIEVE---\");\n",
    "  const lastMessage = messages[messages.length - 1];\n",
    "\n",
    "  if (\"tool_calls\" in lastMessage && Array.isArray(lastMessage.tool_calls) && lastMessage.tool_calls.length) {\n",
    "    console.log(\"---DECISION: RETRIEVE---\");\n",
    "    return \"retrieve\";\n",
    "  }\n",
    "  // If there are no tool calls then we finish.\n",
    "  return END;\n",
    "}\n",
    "\n",
    "/**\n",
    " * Determines whether the Agent should continue based on the relevance of retrieved documents.\n",
    " * This function checks if the last message in the conversation is of type FunctionMessage, indicating\n",
    " * that document retrieval has been performed. It then evaluates the relevance of these documents to the user's\n",
    " * initial question using a predefined model and output parser. If the documents are relevant, the conversation\n",
    " * is considered complete. Otherwise, the retrieval process is continued.\n",
    " * @param {typeof GraphState.State} state - The current state of the agent, including all messages.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} - The updated state with the new message added to the list of messages.\n",
    " */\n",
    "async function gradeDocuments(state: typeof GraphState.State): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---GET RELEVANCE---\");\n",
    "\n",
    "  const { messages } = state;\n",
    "  const tool = {\n",
    "    name: \"give_relevance_score\",\n",
    "    description: \"Give a relevance score to the retrieved documents.\",\n",
    "    schema: z.object({\n",
    "      binaryScore: z.string().describe(\"Relevance score 'yes' or 'no'\"),\n",
    "    })\n",
    "  }\n",
    "\n",
    "  const prompt = ChatPromptTemplate.fromTemplate(\n",
    "    `You are a grader assessing relevance of retrieved docs to a user question.\n",
    "  Here are the retrieved docs:\n",
    "  \\n ------- \\n\n",
    "  {context} \n",
    "  \\n ------- \\n\n",
    "  Here is the user question: {question}\n",
    "  If the content of the docs are relevant to the users question, score them as relevant.\n",
    "  Give a binary score 'yes' or 'no' score to indicate whether the docs are relevant to the question.\n",
    "  Yes: The docs are relevant to the question.\n",
    "  No: The docs are not relevant to the question.`,\n",
    "  );\n",
    "\n",
    "  const model = new ChatOpenAI({\n",
    "    model: \"gpt-4o\",\n",
    "    temperature: 0,\n",
    "  }).bindTools([tool], {\n",
    "    tool_choice: tool.name,\n",
    "  });\n",
    "\n",
    "  const chain = prompt.pipe(model);\n",
    "\n",
    "  const lastMessage = messages[messages.length - 1];\n",
    "\n",
    "  const score = await chain.invoke({\n",
    "    question: messages[0].content as string,\n",
    "    context: lastMessage.content as string,\n",
    "  });\n",
    "\n",
    "  return {\n",
    "    messages: [score]\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Check the relevance of the previous LLM tool call.\n",
    " *\n",
    " * @param {typeof GraphState.State} state - The current state of the agent, including all messages.\n",
    " * @returns {string} - A directive to either \"yes\" or \"no\" based on the relevance of the documents.\n",
    " */\n",
    "function checkRelevance(state: typeof GraphState.State): string {\n",
    "  console.log(\"---CHECK RELEVANCE---\");\n",
    "\n",
    "  const { messages } = state;\n",
    "  const lastMessage = messages[messages.length - 1];\n",
    "  if (!(\"tool_calls\" in lastMessage)) {\n",
    "    throw new Error(\"The 'checkRelevance' node requires the most recent message to contain tool calls.\")\n",
    "  }\n",
    "  const toolCalls = (lastMessage as AIMessage).tool_calls;\n",
    "  if (!toolCalls || !toolCalls.length) {\n",
    "    throw new Error(\"Last message was not a function message\");\n",
    "  }\n",
    "\n",
    "  if (toolCalls[0].args.binaryScore === \"yes\") {\n",
    "    console.log(\"---DECISION: DOCS RELEVANT---\");\n",
    "    return \"yes\";\n",
    "  }\n",
    "  console.log(\"---DECISION: DOCS NOT RELEVANT---\");\n",
    "  return \"no\";\n",
    "}\n",
    "\n",
    "// Nodes\n",
    "\n",
    "/**\n",
    " * Invokes the agent model to generate a response based on the current state.\n",
    " * This function calls the agent model to generate a response to the current conversation state.\n",
    " * The response is added to the state's messages.\n",
    " * @param {typeof GraphState.State} state - The current state of the agent, including all messages.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} - The updated state with the new message added to the list of messages.\n",
    " */\n",
    "async function agent(state: typeof GraphState.State): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---CALL AGENT---\");\n",
    "  \n",
    "  const { messages } = state;\n",
    "  // Find the AIMessage which contains the `give_relevance_score` tool call,\n",
    "  // and remove it if it exists. This is because the agent does not need to know\n",
    "  // the relevance score.\n",
    "  const filteredMessages = messages.filter((message) => {\n",
    "    if (\"tool_calls\" in message && Array.isArray(message.tool_calls) && message.tool_calls.length > 0) {\n",
    "      return message.tool_calls[0].name !== \"give_relevance_score\";\n",
    "    }\n",
    "    return true;\n",
    "  });\n",
    "\n",
    "  const model = new ChatOpenAI({\n",
    "    model: \"gpt-4o\",\n",
    "    temperature: 0,\n",
    "    streaming: true,\n",
    "  }).bindTools(tools);\n",
    "\n",
    "  const response = await model.invoke(filteredMessages);\n",
    "  return {\n",
    "    messages: [response],\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Transform the query to produce a better question.\n",
    " * @param {typeof GraphState.State} state - The current state of the agent, including all messages.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} - The updated state with the new message added to the list of messages.\n",
    " */\n",
    "async function rewrite(state: typeof GraphState.State): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---TRANSFORM QUERY---\");\n",
    "\n",
    "  const { messages } = state;\n",
    "  const question = messages[0].content as string;\n",
    "  const prompt = ChatPromptTemplate.fromTemplate(\n",
    "    `Look at the input and try to reason about the underlying semantic intent / meaning. \\n \n",
    "Here is the initial question:\n",
    "\\n ------- \\n\n",
    "{question} \n",
    "\\n ------- \\n\n",
    "Formulate an improved question:`,\n",
    "  );\n",
    "\n",
    "  // Grader\n",
    "  const model = new ChatOpenAI({\n",
    "    model: \"gpt-4o\",\n",
    "    temperature: 0,\n",
    "    streaming: true,\n",
    "  });\n",
    "  const response = await prompt.pipe(model).invoke({ question });\n",
    "  return {\n",
    "    messages: [response],\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Generate answer\n",
    " * @param {typeof GraphState.State} state - The current state of the agent, including all messages.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} - The updated state with the new message added to the list of messages.\n",
    " */\n",
    "async function generate(state: typeof GraphState.State): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---GENERATE---\");\n",
    "  \n",
    "  const { messages } = state;\n",
    "  const question = messages[0].content as string;\n",
    "  // Extract the most recent ToolMessage\n",
    "  const lastToolMessage = messages.slice().reverse().find((msg) => msg._getType() === \"tool\");\n",
    "  if (!lastToolMessage) {\n",
    "    throw new Error(\"No tool message found in the conversation history\");\n",
    "  }\n",
    "\n",
    "  const docs = lastToolMessage.content as string;\n",
    "\n",
    "  const prompt = await pull<ChatPromptTemplate>(\"rlm/rag-prompt\");\n",
    "\n",
    "  const llm = new ChatOpenAI({\n",
    "    model: \"gpt-4o\",\n",
    "    temperature: 0,\n",
    "    streaming: true,\n",
    "  });\n",
    "\n",
    "  const ragChain = prompt.pipe(llm);\n",
    "\n",
    "  const response = await ragChain.invoke({\n",
    "    context: docs,\n",
    "    question,\n",
    "  });\n",
    "\n",
    "  return {\n",
    "    messages: [response],\n",
    "  };\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph\n",
    "\n",
    "- Start with an agent, `callModel`\n",
    "- Agent make a decision to call a function\n",
    "- If so, then `action` to call tool (retriever)\n",
    "- Then call agent with the tool output added to messages (`state`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { StateGraph } from \"@langchain/langgraph\";\n",
    "\n",
    "// Define the graph\n",
    "const workflow = new StateGraph(GraphState)\n",
    "  // Define the nodes which we'll cycle between.\n",
    "  .addNode(\"agent\", agent)\n",
    "  .addNode(\"retrieve\", toolNode)\n",
    "  .addNode(\"gradeDocuments\", gradeDocuments)\n",
    "  .addNode(\"rewrite\", rewrite)\n",
    "  .addNode(\"generate\", generate);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { START } from \"@langchain/langgraph\";\n",
    "\n",
    "// Call agent node to decide to retrieve or not\n",
    "workflow.addEdge(START, \"agent\");\n",
    "\n",
    "// Decide whether to retrieve\n",
    "workflow.addConditionalEdges(\n",
    "  \"agent\",\n",
    "  // Assess agent decision\n",
    "  shouldRetrieve,\n",
    ");\n",
    "\n",
    "workflow.addEdge(\"retrieve\", \"gradeDocuments\");\n",
    "\n",
    "// Edges taken after the `action` node is called.\n",
    "workflow.addConditionalEdges(\n",
    "  \"gradeDocuments\",\n",
    "  // Assess agent decision\n",
    "  checkRelevance,\n",
    "  {\n",
    "    // Call tool node\n",
    "    yes: \"generate\",\n",
    "    no: \"rewrite\", // placeholder\n",
    "  },\n",
    ");\n",
    "\n",
    "workflow.addEdge(\"generate\", END);\n",
    "workflow.addEdge(\"rewrite\", \"agent\");\n",
    "\n",
    "// Compile\n",
    "const app = workflow.compile();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---CALL AGENT---\n",
      "---DECIDE TO RETRIEVE---\n",
      "---DECISION: RETRIEVE---\n",
      "Output from node: 'agent'\n",
      "{\n",
      "  type: 'ai',\n",
      "  content: '',\n",
      "  tool_calls: [\n",
      "    {\n",
      "      name: 'retrieve_blog_posts',\n",
      "      args: { query: 'types of agent memory' },\n",
      "      id: 'call_adLYkV7T2ry1EZFboT0jPuwn',\n",
      "      type: 'tool_call'\n",
      "    }\n",
      "  ]\n",
      "}\n",
      "---\n",
      "\n",
      "Output from node: 'retrieve'\n",
      "{\n",
      "  type: 'tool',\n",
      "  content: 'Agent System Overview\\n' +\n",
      "    '                \\n' +\n",
      "    '                    Component One: Planning\\n' +\n",
      "    '                        \\n' +\n",
      "    '                \\n' +\n",
      "    '                    Task Decomposition\\n' +\n",
      "    '                \\n' +\n",
      "    '                    Self-Reflection\\n' +\n",
      "    '                \\n' +\n",
      "    '                \\n' +\n",
      "    '                    Component Two: Memory\\n' +\n",
      "    '                        \\n' +\n",
      "    '                \\n' +\n",
      "    '                    Types of Memory\\n' +\n",
      "    '                \\n' +\n",
      "    '                    Maximum Inner Product Search (MIPS)\\n' +\n",
      "    '\\n' +\n",
      "    'Memory stream: is a long-term memory module (external database) that records a comprehensive list of agents’ experience in natural language.\\n' +\n",
      "    '\\n' +\n",
      "    'Each element is an observation, an event directly provided by the agent.\\n' +\n",
      "    '- Inter-agent communication can trigger new natural language statements.\\n' +\n",
      "    '\\n' +\n",
      "    '\\n' +\n",
      "    'Retrieval model: surfaces the context to inform the agent’s behavior, according to relevance, recency and importance.\\n' +\n",
      "    '\\n' +\n",
      "    'Planning\\n' +\n",
      "    '\\n' +\n",
      "    'Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\n' +\n",
      "    'Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n' +\n",
      "    '\\n' +\n",
      "    '\\n' +\n",
      "    'Memory\\n' +\n",
      "    '\\n' +\n",
      "    'The design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.',\n",
      "  tool_calls: undefined\n",
      "}\n",
      "---\n",
      "\n",
      "---GET RELEVANCE---\n",
      "---CHECK RELEVANCE---\n",
      "---DECISION: DOCS NOT RELEVANT---\n",
      "Output from node: 'gradeDocuments'\n",
      "{\n",
      "  type: 'ai',\n",
      "  content: '',\n",
      "  tool_calls: [\n",
      "    {\n",
      "      name: 'give_relevance_score',\n",
      "      args: { binaryScore: 'no' },\n",
      "      type: 'tool_call',\n",
      "      id: 'call_AGE7gORVFubExfJWcjb0C2nV'\n",
      "    }\n",
      "  ]\n",
      "}\n",
      "---\n",
      "\n",
      "---TRANSFORM QUERY---\n",
      "Output from node: 'rewrite'\n",
      "{\n",
      "  type: 'ai',\n",
      "  content: \"What are the different types of agent memory described in Lilian Weng's blog post?\",\n",
      "  tool_calls: []\n",
      "}\n",
      "---\n",
      "\n",
      "---CALL AGENT---\n",
      "---DECIDE TO RETRIEVE---\n",
      "Output from node: 'agent'\n",
      "{\n",
      "  type: 'ai',\n",
      "  content: \"Lilian Weng's blog post describes the following types of agent memory:\\n\" +\n",
      "    '\\n' +\n",
      "    '1. **Memory Stream**:\\n' +\n",
      "    '   - This is a long-term memory module (external database) that records a comprehensive list of agents’ experiences in natural language.\\n' +\n",
      "    '   - Each element in the memory stream is an observation or an event directly provided by the agent.\\n' +\n",
      "    '   - Inter-agent communication can trigger new natural language statements to be added to the memory stream.\\n' +\n",
      "    '\\n' +\n",
      "    '2. **Retrieval Model**:\\n' +\n",
      "    '   - This model surfaces the context to inform the agent’s behavior based on relevance, recency, and importance.\\n' +\n",
      "    '\\n' +\n",
      "    'These memory types are part of a broader design that combines generative agents with memory, planning, and reflection mechanisms to enable agents to behave based on past experiences and interact with other agents.',\n",
      "  tool_calls: []\n",
      "}\n",
      "---\n",
      "\n",
      "{\n",
      "  \"messages\": [\n",
      "    {\n",
      "      \"lc\": 1,\n",
      "      \"type\": \"constructor\",\n",
      "      \"id\": [\n",
      "        \"langchain_core\",\n",
      "        \"messages\",\n",
      "        \"AIMessageChunk\"\n",
      "      ],\n",
      "      \"kwargs\": {\n",
      "        \"content\": \"Lilian Weng's blog post describes the following types of agent memory:\\n\\n1. **Memory Stream**:\\n   - This is a long-term memory module (external database) that records a comprehensive list of agents’ experiences in natural language.\\n   - Each element in the memory stream is an observation or an event directly provided by the agent.\\n   - Inter-agent communication can trigger new natural language statements to be added to the memory stream.\\n\\n2. **Retrieval Model**:\\n   - This model surfaces the context to inform the agent’s behavior based on relevance, recency, and importance.\\n\\nThese memory types are part of a broader design that combines generative agents with memory, planning, and reflection mechanisms to enable agents to behave based on past experiences and interact with other agents.\",\n",
      "        \"additional_kwargs\": {},\n",
      "        \"response_metadata\": {\n",
      "          \"estimatedTokenUsage\": {\n",
      "            \"promptTokens\": 280,\n",
      "            \"completionTokens\": 155,\n",
      "            \"totalTokens\": 435\n",
      "          },\n",
      "          \"prompt\": 0,\n",
      "          \"completion\": 0,\n",
      "          \"finish_reason\": \"stop\",\n",
      "          \"system_fingerprint\": \"fp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3bfp_3cd8b62c3b\"\n",
      "        },\n",
      "        \"tool_call_chunks\": [],\n",
      "        \"id\": \"chatcmpl-9zAaVQGmTLiCaFvtbxUK60qMFsSmU\",\n",
      "        \"usage_metadata\": {\n",
      "          \"input_tokens\": 363,\n",
      "          \"output_tokens\": 156,\n",
      "          \"total_tokens\": 519\n",
      "        },\n",
      "        \"tool_calls\": [],\n",
      "        \"invalid_tool_calls\": []\n",
      "      }\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import { HumanMessage } from \"@langchain/core/messages\";\n",
    "\n",
    "const inputs = {\n",
    "  messages: [\n",
    "    new HumanMessage(\n",
    "      \"What are the types of agent memory based on Lilian Weng's blog post?\",\n",
    "    ),\n",
    "  ],\n",
    "};\n",
    "let finalState;\n",
    "for await (const output of await app.stream(inputs)) {\n",
    "  for (const [key, value] of Object.entries(output)) {\n",
    "    const lastMsg = output[key].messages[output[key].messages.length - 1];\n",
    "    console.log(`Output from node: '${key}'`);\n",
    "    console.dir({\n",
    "      type: lastMsg._getType(),\n",
    "      content: lastMsg.content,\n",
    "      tool_calls: lastMsg.tool_calls,\n",
    "    }, { depth: null });\n",
    "    console.log(\"---\\n\");\n",
    "    finalState = value;\n",
    "  }\n",
    "}\n",
    "\n",
    "console.log(JSON.stringify(finalState, null, 2));"
   ]
  }
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